Summary of Leveraging Continuously Differentiable Activation Functions For Learning in Quantized Noisy Environments, by Vivswan Shah and Nathan Youngblood
Leveraging Continuously Differentiable Activation Functions for Learning in Quantized Noisy Environments
by Vivswan Shah, Nathan Youngblood
First submitted to arxiv on: 4 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers explore the impact of noise on deep learning models in analog systems. They find that differentiable activations like GELU and SiLU can help mitigate quantization error and improve model convergence and accuracy. The authors analyze and train convolutional, linear, and transformer networks with quantized noise and show that continuously differentiable activation functions are more robust to noise than conventional rectified activations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper is about making deep learning models work better in noisy environments. Imagine trying to talk to someone through a walkie-talkie with lots of static – it’s hard to understand what they’re saying! Analog computers can be like that too, but the researchers found ways to make them work better by using special kinds of “on/off” switches for their signals. This could help with all sorts of machine learning tasks, from recognizing pictures to processing sounds. |
Keywords
* Artificial intelligence * Deep learning * Machine learning * Quantization * Transformer
